


import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


__magic_name__  	=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]







def 							__magic_name__    (     lowerCAmelCase_):
	'''simple docstring'''


	for pegasus_name, hf_name in PATTERNS:
		lowerCamelCase_					:				List[str]              =  k.replace(_lowercase     ,	_lowercase)
	return k







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				List[Any]              =  DEFAULTS.copy()
	cfg_kwargs.update(_lowercase)
	lowerCamelCase_					:				Optional[int]              =  PegasusConfig(**_lowercase)
	lowerCamelCase_					:				Any              =  PegasusForConditionalGeneration(_lowercase)
	lowerCamelCase_					:				Optional[Any]              =  torch_model.model.state_dict()
	lowerCamelCase_					:				List[Any]              =  {}
	for k, v in tf_weights.items():
		lowerCamelCase_					:				Union[str, Any]              =  rename_state_dict_key(_lowercase)
		if new_k not in sd:
			raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")

		if "dense" in k or "proj" in new_k:
			lowerCamelCase_					:				Tuple              =  v.T
		lowerCamelCase_					:				Optional[int]              =  torch.tensor(_lowercase     ,	dtype=sd[new_k].dtype)
		assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
	# make sure embedding.padding_idx is respected
	lowerCamelCase_					:				Union[str, Any]              =  torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1])
	lowerCamelCase_					:				Dict              =  mapping["shared.weight"]
	lowerCamelCase_					:				Any              =  mapping["shared.weight"]
	lowerCamelCase_					:				List[Any]              =  {k: torch.zeros_like(_lowercase) for k, v in sd.items() if k.endswith("bias") and k not in mapping}
	mapping.update(**_lowercase)
	lowerCamelCase_       ,lowerCamelCase_					:				Dict              =  torch_model.model.load_state_dict(_lowercase     ,	strict=_lowercase)
	lowerCamelCase_					:				Union[str, Any]              =  [
	    k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
	]
	assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
	assert extra == [], F"""no matches found for the following tf keys {extra}"""
	return torch_model







def 							__magic_name__    (     lowerCAmelCase_="./ckpt/aeslc/model.ckpt-32000"):
	'''simple docstring'''


	lowerCamelCase_					:				List[Any]              =  tf.train.list_variables(_lowercase)
	lowerCamelCase_					:				Tuple              =  {}
	lowerCamelCase_					:				Union[str, Any]              =  ["Adafactor", "global_step"]
	for name, shape in tqdm(_lowercase     ,	desc="converting tf checkpoint to dict"):
		lowerCamelCase_					:				Optional[Any]              =  any(pat in name for pat in ignore_name)
		if skip_key:
			continue
		lowerCamelCase_					:				str              =  tf.train.load_variable(_lowercase     ,	_lowercase)
		lowerCamelCase_					:				List[str]              =  array
	return tf_weights







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				Dict              =  Path(_lowercase).parent.name
	lowerCamelCase_					:				Dict              =  task_specific_params[F"""summarization_{dataset}"""]["max_position_embeddings"]
	lowerCamelCase_					:				Tuple              =  PegasusTokenizer.from_pretrained("sshleifer/pegasus"     ,	model_max_length=_lowercase)
	assert tok.model_max_length == desired_max_model_length
	tok.save_pretrained(_lowercase)

	# convert model
	lowerCamelCase_					:				Dict              =  get_tf_weights_as_numpy(_lowercase)
	lowerCamelCase_					:				List[str]              =  task_specific_params[F"""summarization_{dataset}"""]
	if dataset == "large":
		lowerCamelCase_					:				int              =  task_specific_params
	lowerCamelCase_					:				Tuple              =  convert_pegasus(_lowercase     ,	_lowercase)
	torch_model.save_pretrained(_lowercase)
	lowerCamelCase_					:				List[Any]              =  torch_model.state_dict()
	sd.pop("model.decoder.embed_positions.weight")
	sd.pop("model.encoder.embed_positions.weight")
	torch.save(_lowercase     ,	Path(_lowercase) / "pytorch_model.bin")


if __name__ == "__main__":
							__magic_name__  	=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
							parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
							__magic_name__  	=     parser.parse_args()
							if args.save_dir is None:
														__magic_name__  	=     Path(args.tf_ckpt_path).parent.name
														__magic_name__  	=     os.path.join('''pegasus''', dataset)
							convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)





"""simple docstring"""



import math
from datetime import datetime, timedelta
def      UpperCAmelCase     (       _lowercase :   int	)						-> datetime:


	"""simple docstring"""




	lowerCAmelCase_      							=			year % 1_9
	lowerCAmelCase_      							=			year % 4
	lowerCAmelCase_      							=			year % 7
	lowerCAmelCase_      							=			math.floor(year / 1_0_0	)
	lowerCAmelCase_      							=			math.floor((1_3 + 8 * leap_day_inhibits) / 2_5	)
	lowerCAmelCase_      							=			leap_day_inhibits / 4
	lowerCAmelCase_      							=			(
	    1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
	) % 3_0
	lowerCAmelCase_      							=			(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

	# days to be added to March 21
	lowerCAmelCase_      							=			(1_9 * metonic_cycle + secular_moon_shift) % 3_0

	# PHM -> Paschal Full Moon
	lowerCAmelCase_      							=			(
	    2 * julian_leap_year
	    + 4 * non_leap_year
	    + 6 * days_to_add
	    + century_starting_point
	) % 7

	if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_9	)
	elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
		return datetime(_lowercase			,   4			,   1_8	)
	else:
		return datetime(_lowercase			,   3			,   2_2	) + timedelta(
		    days=int(days_to_add + days_from_phm_to_sunday	)	)


if __name__ == "__main__":
		for year in (19_94, 20_00, 20_10, 20_21, 20_23):
				lowercase_							=							'will be' if year > datetime.now().year else 'was'
				print(f"""Easter in {year} {tense} {gauss_easter(year)}""")